Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/

1. The Good, the Bad, and the Ugly
Glenn Platkin & Kaige Liu
July 9, 2020

2. Agenda
 Introduction
 Snowflake: The Good, the Bad, and the Ugly
 Kyligence + Snowflake Joint Solution
 Demo
 Technical Deep Dive
 Q&A

3. © Kyligence Inc. 2020, Confidential.
Who Am I?
30 years in software
• Computer Associates
• Business Objects
• Actuate
• Greenplum
• Kognitio
• 1010data
• Kyligence

4. © Kyligence Inc. 2020, Confidential.
Who Am I?
30 years in software
• Computer Associates
• Business Objects
• Actuate
• Greenplum
• Kognitio
• 1010data
• Kyligence

5. © Kyligence Inc. 2020, Confidential.

6. © Kyligence Inc. 2020, Confidential.
What I've Seen
Is it the 1980s or Today?
Common Themes
• A LOT of data
• Slow hardware
• Difficult/limited tools
• Unhappy users
And Yet
• Users want even more data
• Hardware has its limits
• Too many tools
• Upgrades/conversions/legacy
• Cost/time/training
• DBA’s render the data useless
• True analytics = detail + history
• Users are still unhappy
Reaction
• Newest shiny BI/reporting tool
• More/bigger/faster hardware
• Team of DBA's
indexing/aggregating/limit
history

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Summary
We've Come a Long Way . . . or Have We?
Hadoop/Spark Clusters
Columnar DBMS
In-Memory
Appliances
DB Machines
BI Tools
Analytics Tools
Mainframe
Tape/Files/Databases
Cobol/RPG/4-GL's
Minicomputer
Relational DBMS
Reporting Tools
Client-Server
Data Marts/Warehouse
BI/Multi-Dimensional Tools
Cloud
Next-Generation Analytics
Machine Learning
~1980s 1990s 2000s 2005 2010 2020

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• Easy to Get Started
• Low Cost of Entry
• Secure
• Intuitive
• Autonomy/Control
Snowflake – The Good
• Consolidated Data
• Flexibility
• Less Management
• Reduced DBA
• Transparent

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The Promise
• Lots of detail, no more aggregations
• Lots of history, no more
limitations
• Use your tool of choice
• Finally – True Analytics
• No Impact on Production
• Happy Users
Snowflake – The Bad
The Reality
• The more data you have,
• The greater its complexity,
• The higher your concurrency:
• The greater the impact on performance
• Requiring additional compute
• DBA's to aggregate
• Higher monthly cost
Especially for Enterprise Accounts
At best, poor/mediocre results

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Snowflake – The Ugly
Your Snowflake Monthly Statement
• Pay-per-Use Model
• GREAT – Just pay for what I use!!
• You pay for:
• Storage, Compute, Users
• B.Y.O.C. – Not an option, because there is too much profit
• Up-Lift: Standard, Premier, Enterprise, Premium, Business Critical
Do they care if your queries take longer?
Do they have a financial incentive to speed up your queries?
Especially for Enterprise Accounts

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Top-3 Most Common Issues with Snowflake
Have we gone full circle, AGAIN?
#3. NOT Purpose-Built for Big Data / Performance
• Require M/B/Trillions of Rows of Detail
• High-Concurrency
• Complex Requests
• Unhappy Users

12. © Kyligence Inc. 2020, Confidential.
Top-3 Most Common Issues with Snowflake
Have we gone full circle, AGAIN?
#3. NOT Purpose-Built for Big Data / Performance
• Require M/B/Trillions of Rows of Detail
• High-Concurrency
• Complex Requests
• Unhappy Users
#2. Limited Remedies
• More Compute
• Limit Data
• DBA's Indexing/Aggregating

13. © Kyligence Inc. 2020, Confidential.
Top-3 Most Common Issues with Snowflake
Have we gone full circle, AGAIN?
#3. NOT Purpose-Built for Big Data / Performance
• Require M/B/Trillions of Rows of Detail
• High-Concurrency
• Complex Requests
• Unhappy Users
#2. Limited Remedies
• More Compute
• Limit Data
• DBA's Indexing/Aggregating
#1. Your Snowflake Monthly Statement
• Out-of-Control Costs
• Mediocre Results
• Unhappy CFO

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Dilemma . . .
Requirements & Challenges
• Teradata
• Many Petabytes of Detail Data
• Complex Queries
• Many Users/High Concurrency
• Using a Variety of BI/Reporting/Analysis Tools
• High Performance at Any Scale

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Requirements & Challenges
• Teradata
• Many Petabytes of Detail Data
• Complex Queries
• Many Users/High Concurrency
• Using a Variety of BI/Reporting/Analysis Tools
• High Performance at Any Scale
Solution . . .
Plan A:
• What was commercially available?
• Netezza, Greenplum, Vertica,
Postgres, others
• None could scale
• None could perform
• All were too complex to
implement and maintain
Plan B:
• Build

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Solution
• OLAP / Pre-Compute
• Ultra-High Performance
• High Concurrency
• No Performance Degradation
• Reduced Complexity
• Easily Handles Complex Queries
• Slice/Dice/Pivot
• Supports BI Tools - SQL
• Unlimited Scale
• Data/Storage
• Compute
• Users/Concurrency
• Low Management
• Easy Implementation
• Cost-Effective

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Apache Kylin
• 2013 - Created at eBay
• 2014 - Apache Open Source Foundation
• 2015 - Graduated Top-Level Project
• 2016 - Kylin ---> Kyligence
• Kyligence Enterprise
• Kyligence Cloud
• Kyligence
• Original Kylin members
• Still maintains/manages the Kylin community
• Most active contributors to Kylin

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Kyligence
• Founded in 2016 by the creators of Apache Kylin
• Built around Kylin with augmented AI, enhanced to deliver
unprecedented enterprise analytic performance
• CRN Top-10 big data startups in 2018
• Global Presence: San Jose, Seattle, New York, Shanghai, Beijing
• VCs: Fidelity International, Shunwei Capital, Broadband Capital,
Redpoint, Cisco, Coatue
2016
Founded Pre-A
Redpoint
Cisco
2017
Series A
CBC
Shunwei
2018
Series B
8Roads
2019
Series C
Coatue

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1,000+ Users Worldwide

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Kyligence Architecture

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High-Performance & Concurrency

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Pre-Computation vs. Online Calculation

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AI-Augmented Engine

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Intelligent Query Routing & Pushdown

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Kyligence for ALL

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Enterprise-Grade Access Control

27. Kyligence
+
Snowflake

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Joint Solution: Kyligence + Snowflake
Applications
Semantic and
Augmented Models
Data Warehouse
Data Source
Benefits
• Ultra-low latency for
aggregated queries
• High concurrency
• Unified Semantic Layer
• Support Excel Pivot table
and other MDX scenarios
• Cost effective
Database Events Files IoT
Unified Semantic Layer
BI
Integration
Access
Control
Enterprise
Security
AI-Based
Indexes
Scalable Query
Engine Augmented Models

29. Demo

30. Performance TCOSemantic Layer
Deep Dive
Performanc
e
TCO
Semantic
Layer

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Kyligence Cloud - Accelerate Mission-Critical Analytics
Intelligently
• Unified Query Entrance
ODBC/JDBC API/SDK
Finance Marketing Sales Customer Checkout
Aggregate Index
10%4% 80%
Data
Lake
SQL/MDX
Semantic Services
6%
Distributed
Query Engine
AI-Augmented
Engine
Smart
Pushdown
Metadata
Management
Enterprise
Security
• Business Semantic Layer
• Query Pattern for All Data
• High-Performance Engine

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AI-Augmented Engine — Learn From Your Analytics
History

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AI-Augmented Engine — Learn From Your Analytics
History

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Under the Hood: Smart Cuboids
• Each model consists of N-dimension cuboids, which is a combination of several
dimensions in different permutations and combinations.
• Apache Spark is used to build the cuboids, making query results extremely fast.
• When the user sends a query, the model intelligently looks for the
cuboid/segment and quickly returns the results.

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Kyligence - A Paradigm Shift in Cloud Analytics
O(N)
O(1)
Data Volume
Response time
Pre-Computation
Online Calculation
Consistent Ultra-low Latency by Kyligence

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TPCH Benchmark
SF=50
Query Response Time | 0.5 Billion
SF=500
Query Response Time | 5 Billion
• No warmup
• Lower is better
• Run each query 3 times
• Record the average time
For Each Dataset:

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Unified Semantic Layer
Unified Semantic Layer
Hierarchies/KPIs/Calculated Measures/Alias
Excel Tableau PowerBI

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Elastic Scaling — Handle Peak Time Automatically
• Fewer compute and storage
resources utilized
• Dynamic on-demand cluster resizing
• Uses spot instances
• Efficient planning for data growth

39. © Kyligence Inc. 2020, Confidential.
TCO Benchmark
• Build once, query anytime
• More queries, cheaper
• Will not increase sharply when
dealing with high concurrency

40. Summary

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Top-3 Most Common Issues with Snowflake
#3. NOT Purpose-Built for Big Data Performance Purpose-Built for Big Data & Performance
• BIG Data (billions/trillions of rows of detail)
• High Concurrency
• Ultra-High Performance (sub-second response)
• Complex Requests (Hierarchies, High
Cardinality, COUNT_DISTINCT)
• Flat/Consistent Performance

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Top-3 Most Common Issues with Snowflake
#2. Limited Remedies UNLIMITED Scale
• Unlimited Data/Users/Compute
• Little/No Maintenance
• Secure
• Unified Semantic Layer
• Any SQL/MDX-Based Tools & Excel

43. © Kyligence Inc. 2020, Confidential.
Top-3 Most Common Issues with Snowflake
#1. Your Snowflake Monthly Statement
#2. Limited Remedies
#3. NOT Purpose-Built for Big Data / Performance
Cost-Effective/Fixed-Cost
• B.Y.O.C.
• Sensible Subscription-Based Licensing
• Unlimited Users
• Unlimited DW/DL Size
• Fee Based on "Raw/Consumed Data"
• Not all data is consumed
• Detail is not, it's indexed
• Consumed data is used for pre-compute
• Small Required Storage

44. © Kyligence Inc. 2020, Confidential.
Kyligence + Snowflake = Perfect Couple
Right Solution for the Correct Challenge
• B.Y.O.C
• Any SQL/MDX-Based Tool
• Snowflake/Any Data Source
• Limitless Scale
• Ultra-High Performance
• True Analytics
• Unified Semantic Layer
• Simple, Easy, Predictable Licensing and
Cost-Effective
• Perfect Complement to Snowflake!

45. Test Drive Kyligence – Free!
www.kyligence.io/free-
trial/

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Test Drive Kyligence for Free Today: https://kyligence.io/free-
trial/
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